Quantization constrained neural image coding

ABSTRACT

Artificial image generation may include obtaining a source image, identifying quantization information from the source image, wherein identifying the quantization information includes identifying multiresolution quantization interval information from the source image, generating a restoration filtered image by restoration filtering the source image, generating a constrained restoration filtered image by constraining the restoration filtered image based on the quantization information, obtaining an unconstrained artificial image based on the constrained restoration filtered image and a generative artificial neural network obtained using a generative adversarial network, obtaining the artificial image by constraining the unconstrained artificial image based on the quantization information, and outputting the artificial image.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to and the benefit of U.S. applicationpatent Ser. No. 16/430,889, filed Jun. 4, 2019, now U.S. Pat. No.11,166,022, the entire disclosure of which is hereby incorporated byreference.

BACKGROUND

Digital images and video can be used, for example, on the internet, forremote business meetings via video conferencing, high-definition videoentertainment, video advertisements, or sharing of user-generatedcontent. Due to the large amount of data involved in transferring andprocessing image and video data, high-performance compression may beadvantageous for transmission and storage. Accordingly, it would beadvantageous to provide high-resolution image and video transmitted overcommunications channels having limited bandwidth, such as image andvideo coding using quantization constrained neural image coding.

SUMMARY

This application relates to encoding and decoding of image data, videostream data, or both for transmission or storage. Disclosed herein areaspects of systems, methods, and apparatuses for encoding and decodingusing quantization constrained neural image coding.

An aspect is a method for image coding using quantization constrainedneural image coding. Image coding using quantization constrained neuralimage coding may include generating, by a processor, an artificial imageand outputting the artificial image. Generating the artificial image mayinclude obtaining a source image, identifying quantization informationfrom the source image, wherein identifying the quantization informationincludes identifying multiresolution quantization interval informationfrom the source image, generating a restoration filtered image byrestoration filtering the source image, generating a constrainedrestoration filtered image by constraining the restoration filteredimage based on the quantization information, obtaining an unconstrainedartificial image based on the constrained restoration filtered image anda generative artificial neural network obtained using a generativeadversarial network, and obtaining the artificial image by constrainingthe unconstrained artificial image based on the quantizationinformation.

Another aspect is a method for image coding using quantizationconstrained neural image coding. Image coding using quantizationconstrained neural image coding may include generating, by a processor,an artificial image and outputting the artificial image. Generating theartificial image may include obtaining a source image, identifyingquantization information from the source image, and obtaining theartificial image based on the source image, the quantizationinformation, and a machine learning model.

Another aspect is a method for image coding using quantizationconstrained neural image coding. Image coding using quantizationconstrained neural image coding may include generating, by a processor,an artificial image and outputting the artificial image. Generating theartificial image may include obtaining a source image, identifyingquantization information from the source image, and generating aconstrained restoration filtered image based on a defined cardinality ofiterations of constrained restoration filtered image generation. Eachiteration of constrained restoration filtered image generation mayinclude generating a restoration filtered image by restoration filteringa restoration filtering input image, wherein, on a condition that theiteration of constrained restoration filtered image generation is afirst iteration of constrained restoration filtered image generation,using the source image as the restoration filtering input image, on acondition that the iteration of constrained restoration filtered imagegeneration is an iteration of constrained restoration filtered imagegeneration subsequent to the first iteration of constrained restorationfiltered image generation, using the constrained restoration filteredimage obtained by an immediately preceding iteration of constrainedrestoration filtered image generation as the restoration filtering inputimage, and constraining the restoration filtered image based on thequantization information to obtain the constrained restoration filteredimage. Generating the artificial image may include obtaining theartificial image based on an artificial image generation input image,the quantization information, and a generative artificial neuralnetwork, wherein obtaining the artificial image includes a definedcardinality of iterations of artificial image generation. Each iterationof the artificial image generation may include inputting the artificialimage generation input image to the generative artificial neuralnetwork, wherein, on a condition that the iteration of the artificialimage generation is a first iteration of artificial image generation,using the constrained restoration filtered image as the artificial imagegeneration input image, on a condition that the iteration of theartificial image generation is an iteration of artificial imagegeneration subsequent to the first iteration of artificial imagegeneration, using the artificial image obtained by an immediatelypreceding iteration of artificial image generation as the artificialimage generation input image, in response to inputting the artificialimage generation input image to the generative artificial neuralnetwork, obtaining an unconstrained artificial image from the generativeartificial neural network, and constraining the unconstrained artificialimage based on the quantization information to obtain the artificialimage.

Another aspect is an apparatus for image coding using quantizationconstrained neural image coding. The apparatus may include a processorconfigured to generate an artificial image and output the artificialimage. The processor may be configured to generate the artificial imageby obtaining a source image, identifying quantization information fromthe source image, wherein identifying the quantization informationincludes identifying multiresolution quantization interval informationfrom the source image, generating a restoration filtered image byrestoration filtering the source image, generating a constrainedrestoration filtered image by constraining the restoration filteredimage based on the quantization information, obtaining an unconstrainedartificial image based on the constrained restoration filtered image anda generative artificial neural network obtained using a generativeadversarial network, and obtaining the artificial image by constrainingthe unconstrained artificial image based on the quantizationinformation.

Another aspect is an apparatus for image coding using quantizationconstrained neural image coding. The apparatus may include a processorconfigured to generate an artificial image and output the artificialimage. The processor may be configured to generate the artificial imageby obtaining a source image, identifying quantization information fromthe source image, and obtaining the artificial image based on the sourceimage, the quantization information, and a machine learning model.

Another aspect is an apparatus for image coding using quantizationconstrained neural image coding. The apparatus may include a processorconfigured to generate an artificial image and output the artificialimage. The processor may be configured to generate the artificial imageby obtaining a source image, identifying quantization information fromthe source image, and generating a constrained restoration filteredimage based on a defined cardinality of iterations of constrainedrestoration filtered image generation. Each iteration of constrainedrestoration filtered image generation may include generating arestoration filtered image by restoration filtering a restorationfiltering input image, wherein, on a condition that the iteration ofconstrained restoration filtered image generation is a first iterationof constrained restoration filtered image generation, using the sourceimage as the restoration filtering input image, on a condition that theiteration of constrained restoration filtered image generation is aniteration of constrained restoration filtered image generationsubsequent to the first iteration of constrained restoration filteredimage generation, using the constrained restoration filtered imageobtained by an immediately preceding iteration of constrainedrestoration filtered image generation as the restoration filtering inputimage, and constraining the restoration filtered image based on thequantization information to obtain the constrained restoration filteredimage. Generating the artificial image may include obtaining theartificial image based on an artificial image generation input image,the quantization information, and a generative artificial neuralnetwork, wherein obtaining the artificial image includes a definedcardinality of iterations of artificial image generation. Each iterationof the artificial image generation may include inputting the artificialimage generation input image to the generative artificial neuralnetwork, wherein, on a condition that the iteration of the artificialimage generation is a first iteration of artificial image generation,using the constrained restoration filtered image as the artificial imagegeneration input image, on a condition that the iteration of theartificial image generation is an iteration of artificial imagegeneration subsequent to the first iteration of artificial imagegeneration, using the artificial image obtained by an immediatelypreceding iteration of artificial image generation as the artificialimage generation input image, in response to inputting the artificialimage generation input image to the generative artificial neuralnetwork, obtaining an unconstrained artificial image from the generativeartificial neural network, and constraining the unconstrained artificialimage based on the quantization information to obtain the artificialimage.

In some implementations, the multiresolution quantization intervalinformation may include respective quantization interval information fortwo or more block sizes (resolutions) from a defined set of block sizes,such as 8×8, 16×16, 32×32, and 64×64. The quantization intervalinformation for a block size may be a bit or a flag and may indicatewhether the top-left transform coefficient (DC value) for the block iswithin the upper or lower portion of the corresponding quantizationinterval or range. In some implementations, an artificial image mayinclude a combination of image detail from an original or source imageand artificial image detail generated using an artificial intelligenceor machine learning model.

Variations in these and other aspects will be described in additionaldetail hereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The description herein makes reference to the accompanying drawingswherein like reference numerals refer to like parts throughout theseveral views unless otherwise noted or otherwise clear from context.

FIG. 1 is a diagram of a computing device in accordance withimplementations of this disclosure.

FIG. 2 is a diagram of a computing and communications system inaccordance with implementations of this disclosure.

FIG. 3 is a diagram of a video stream for use in encoding and decodingin accordance with implementations of this disclosure.

FIG. 4 is a block diagram of an encoder in accordance withimplementations of this disclosure.

FIG. 5 is a block diagram of a decoder in accordance withimplementations of this disclosure.

FIG. 6 is a block diagram of a representation of a portion of a frame inaccordance with implementations of this disclosure.

FIG. 7 is a flowchart diagram of an example of encoding usingmultiresolution quantization interval information in accordance withimplementations of this disclosure.

FIG. 8 is a flowchart diagram of an example of quantization constrainedneural image coding in accordance with implementations of thisdisclosure.

DETAILED DESCRIPTION

Image and video compression schemes may include breaking an image, orframe, into smaller portions, such as blocks, and generating an outputbitstream using techniques to minimize the bandwidth utilization of theinformation included for each block in the output. In someimplementations, the information included for each block in the outputmay be limited by reducing spatial redundancy, reducing temporalredundancy, or a combination thereof. For example, temporal or spatialredundancies may be reduced by predicting a frame, or a portion thereof,based on information available to both the encoder and decoder, andincluding information representing a difference, or residual, betweenthe predicted frame and the original frame in the encoded bitstream. Theresidual information may be further compressed by transforming theresidual information into transform coefficients, quantizing thetransform coefficients, and entropy coding the quantized transformcoefficients. Other coding information, such as motion information, maybe included in the encoded bitstream, which may include transmittingdifferential information based on predictions of the encodinginformation, which may be entropy coded to further reduce thecorresponding bandwidth utilization. An encoded bitstream can be decodedto reconstruct the blocks and the source images from the limitedinformation.

In some implementations, the reconstructed image may include codingartifacts, such as quantization error (or mach banding) artifacts,blocking (or tiling) artifacts, ringing artifacts, or a combinationthereof, which may limit the visual quality of the reconstructed image.In some implementations, the reconstructed image may omit image detailrelative to the input image.

Implementations of quantization constrained neural image coding mayreduce or eliminate coding artifacts. For example, quantizationconstrained neural image coding may include using multiresolutionquantization interval information. Using multiresolution quantizationinterval information may include encoding using multiresolutionquantization interval information and decoding using multiresolutionquantization interval information. Encoding using multiresolutionquantization interval information may include determining quantizationinterval information indicating a correlation between average transformcoefficient values and respective quantization parameters on a blockbasis at multiple resolutions, such as 8×8, 16×16, 32×32, and 64×64, andincluding the multiresolution quantization interval information with theencoded image. Decoding using multiresolution quantization intervalinformation may include decoding the multiresolution quantizationinterval information for an image and using the multiresolutionquantization interval information to reduce or eliminate codingartifacts, such as mach banding. In another example, quantizationconstrained neural image coding may include performing one or moreiterations of image restoration filtering to reduce or eliminate codingartifacts, such as blocking artifacts and ringing artifacts. In anotherexample, quantization constrained neural image coding may include usinga generative artificial neural network to improve image quality,relative to the image restoration filtered image, by adding artificiallyidentified image detail.

FIG. 1 is a diagram of a computing device 100 in accordance withimplementations of this disclosure. The computing device 100 shownincludes a memory 110, a processor 120, a user interface (UI) 130, anelectronic communication unit 140, a sensor 150, a power source 160, anda bus 170. As used herein, the term “computing device” includes anyunit, or a combination of units, capable of performing any method, orany portion or portions thereof, disclosed herein.

The computing device 100 may be a stationary computing device, such as apersonal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.Although shown as a single unit, any one element or elements of thecomputing device 100 can be integrated into any number of separatephysical units. For example, the user interface 130 and processor 120can be integrated in a first physical unit and the memory 110 can beintegrated in a second physical unit.

The memory 110 can include any non-transitory computer-usable orcomputer-readable medium, such as any tangible device that can, forexample, contain, store, communicate, or transport data 112,instructions 114, an operating system 116, or any information associatedtherewith, for use by or in connection with other components of thecomputing device 100. The non-transitory computer-usable orcomputer-readable medium can be, for example, a solid state drive, amemory card, removable media, a read-only memory (ROM), a random-accessmemory (RAM), any type of disk including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, an application-specificintegrated circuits (ASICs), or any type of non-transitory mediasuitable for storing electronic information, or any combination thereof.

Although shown a single unit, the memory 110 may include multiplephysical units, such as one or more primary memory units, such asrandom-access memory units, one or more secondary data storage units,such as disks, or a combination thereof. For example, the data 112, or aportion thereof, the instructions 114, or a portion thereof, or both,may be stored in a secondary storage unit and may be loaded or otherwisetransferred to a primary storage unit in conjunction with processing therespective data 112, executing the respective instructions 114, or both.In some implementations, the memory 110, or a portion thereof, may beremovable memory.

The data 112 can include information, such as input audio data, encodedaudio data, decoded audio data, or the like. The instructions 114 caninclude directions, such as code, for performing any method, or anyportion or portions thereof, disclosed herein. The instructions 114 canbe realized in hardware, software, or any combination thereof. Forexample, the instructions 114 may be implemented as information storedin the memory 110, such as a computer program, that may be executed bythe processor 120 to perform any of the respective methods, algorithms,aspects, or combinations thereof, as described herein.

Although shown as included in the memory 110, in some implementations,the instructions 114, or a portion thereof, may be implemented as aspecial purpose processor, or circuitry, that can include specializedhardware for carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. Portions of the instructions114 can be distributed across multiple processors on the same machine ordifferent machines or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

The processor 120 can include any device or system capable ofmanipulating or processing a digital signal or other electronicinformation now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 120 can include a special purposeprocessor, a central processing unit (CPU), a digital signal processor(DSP), a plurality of microprocessors, one or more microprocessor inassociation with a DSP core, a controller, a microcontroller, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a programmable logic array, programmable logiccontroller, microcode, firmware, any type of integrated circuit (IC), astate machine, or any combination thereof. As used herein, the term“processor” includes a single processor or multiple processors.

The user interface 130 can include any unit capable of interfacing witha user, such as a virtual or physical keypad, a touchpad, a display, atouch display, a speaker, a microphone, a video camera, a sensor, or anycombination thereof. For example, the user interface 130 may be anaudio-visual display device, and the computing device 100 may presentaudio, such as decoded audio, using the user interface 130 audio-visualdisplay device, such as in conjunction with displaying video, such asdecoded video. Although shown as a single unit, the user interface 130may include one or more physical units. For example, the user interface130 may include an audio interface for performing audio communicationwith a user, and a touch display for performing visual and touch-basedcommunication with the user.

The electronic communication unit 140 can transmit, receive, or transmitand receive signals via a wired or wireless electronic communicationmedium 180, such as a radio frequency (RF) communication medium, anultraviolet (UV) communication medium, a visible light communicationmedium, a fiber optic communication medium, a wireline communicationmedium, or a combination thereof. For example, as shown, the electroniccommunication unit 140 is operatively connected to an electroniccommunication interface 142, such as an antenna, configured tocommunicate via wireless signals.

Although the electronic communication interface 142 is shown as awireless antenna in FIG. 1 , the electronic communication interface 142can be a wireless antenna, as shown, a wired communication port, such asan Ethernet port, an infrared port, a serial port, or any other wired orwireless unit capable of interfacing with a wired or wireless electroniccommunication medium 180. Although FIG. 1 shows a single electroniccommunication unit 140 and a single electronic communication interface142, any number of electronic communication units and any number ofelectronic communication interfaces can be used.

The sensor 150 may include, for example, an audio-sensing device, avisible light-sensing device, a motion sensing device, or a combinationthereof. For example, 100 the sensor 150 may include a sound-sensingdevice, such as a microphone, or any other sound-sensing device nowexisting or hereafter developed that can sense sounds in the proximityof the computing device 100, such as speech or other utterances, made bya user operating the computing device 100. In another example, thesensor 150 may include a camera, or any other image-sensing device nowexisting or hereafter developed that can sense an image such as theimage of a user operating the computing device. Although a single sensor150 is shown, the computing device 100 may include a number of sensors150. For example, the computing device 100 may include a first cameraoriented with a field of view directed toward a user of the computingdevice 100 and a second camera oriented with a field of view directedaway from the user of the computing device 100.

The power source 160 can be any suitable device for powering thecomputing device 100. For example, the power source 160 can include awired external power source interface; one or more dry cell batteries,such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 100. Although a singlepower source 160 is shown in FIG. 1 , the computing device 100 mayinclude multiple power sources 160, such as a battery and a wiredexternal power source interface.

Although shown as separate units, the electronic communication unit 140,the electronic communication interface 142, the user interface 130, thepower source 160, or portions thereof, may be configured as a combinedunit. For example, the electronic communication unit 140, the electroniccommunication interface 142, the user interface 130, and the powersource 160 may be implemented as a communications port capable ofinterfacing with an external display device, providing communications,power, or both.

One or more of the memory 110, the processor 120, the user interface130, the electronic communication unit 140, the sensor 150, or the powersource 160, may be operatively coupled via a bus 170. Although a singlebus 170 is shown in FIG. 1 , a computing device 100 may include multiplebuses. For example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,and the bus 170 may receive power from the power source 160 via the bus170. In another example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,the power source 160, or a combination thereof, may communicate data,such as by sending and receiving electronic signals, via the bus 170.

Although not shown separately in FIG. 1 , one or more of the processor120, the user interface 130, the electronic communication unit 140, thesensor 150, or the power source 160 may include internal memory, such asan internal buffer or register. For example, the processor 120 mayinclude internal memory (not shown) and may read data 112 from thememory 110 into the internal memory (not shown) for processing.

Although shown as separate elements, the memory 110, the processor 120,the user interface 130, the electronic communication unit 140, thesensor 150, the power source 160, and the bus 170, or any combinationthereof can be integrated in one or more electronic units, circuits, orchips.

FIG. 2 is a diagram of a computing and communications system 200 inaccordance with implementations of this disclosure. The computing andcommunications system 200 shown includes computing and communicationdevices 100A, 100B, 100C, access points 210A, 210B, and a network 220.For example, the computing and communication system 200 can be amultiple access system that provides communication, such as voice,audio, data, video, messaging, broadcast, or a combination thereof, toone or more wired or wireless communicating devices, such as thecomputing and communication devices 100A, 100B, 100C. Although, forsimplicity, FIG. 2 shows three computing and communication devices 100A,100B, 100C, two access points 210A, 210B, and one network 220, anynumber of computing and communication devices, access points, andnetworks can be used.

A computing and communication device 100A, 100B, 100C can be, forexample, a computing device, such as the computing device 100 shown inFIG. 1 . For example, the computing and communication devices 100A, 100Bmay be user devices, such as a mobile computing device, a laptop, a thinclient, or a smartphone, and the computing and communication device 100Cmay be a server, such as a mainframe or a cluster. Although thecomputing and communication device 100A and the computing andcommunication device 100B are described as user devices, and thecomputing and communication device 100C is described as a server, anycomputing and communication device may perform some or all of thefunctions of a server, some or all of the functions of a user device, orsome or all of the functions of a server and a user device. For example,the server computing and communication device 100C may receive, encode,process, store, transmit, or a combination thereof audio data and one orboth of the computing and communication device 100A and the computingand communication device 100B may receive, decode, process, store,present, or a combination thereof the audio data.

Each computing and communication device 100A, 100B, 100C, which mayinclude a user equipment (UE), a mobile station, a fixed or mobilesubscriber unit, a cellular telephone, a personal computer, a tabletcomputer, a server, consumer electronics, or any similar device, can beconfigured to perform wired or wireless communication, such as via thenetwork 220. For example, the computing and communication devices 100A,100B, 100C can be configured to transmit or receive wired or wirelesscommunication signals. Although each computing and communication device100A, 100B, 100C is shown as a single unit, a computing andcommunication device can include any number of interconnected elements.

Each access point 210A, 210B can be any type of device configured tocommunicate with a computing and communication device 100A, 100B, 100C,a network 220, or both via wired or wireless communication links 180A,180B, 180C. For example, an access point 210A, 210B can include a basestation, a base transceiver station (BTS), a Node-B, an enhanced Node-B(eNode-B), a Home Node-B (HNode-B), a wireless router, a wired router, ahub, a relay, a switch, or any similar wired or wireless device.Although each access point 210A, 210B is shown as a single unit, anaccess point can include any number of interconnected elements.

The network 220 can be any type of network configured to provideservices, such as voice, data, applications, voice over internetprotocol (VoIP), or any other communications protocol or combination ofcommunications protocols, over a wired or wireless communication link.For example, the network 220 can be a local area network (LAN), widearea network (WAN), virtual private network (VPN), a mobile or cellulartelephone network, the Internet, or any other means of electroniccommunication. The network can use a communication protocol, such as thetransmission control protocol (TCP), the user datagram protocol (UDP),the internet protocol (IP), the real-time transport protocol (RTP) theHyperText Transport Protocol (HTTP), or a combination thereof.

The computing and communication devices 100A, 100B, 100C can communicatewith each other via the network 220 using one or more a wired orwireless communication links, or via a combination of wired and wirelesscommunication links. For example, as shown the computing andcommunication devices 100A, 100B can communicate via wirelesscommunication links 180A, 180B, and computing and communication device100C can communicate via a wired communication link 180C. Any of thecomputing and communication devices 100A, 100B, 100C may communicateusing any wired or wireless communication link, or links. For example, afirst computing and communication device 100A can communicate via afirst access point 210A using a first type of communication link, asecond computing and communication device 100B can communicate via asecond access point 210B using a second type of communication link, anda third computing and communication device 100C can communicate via athird access point (not shown) using a third type of communication link.Similarly, the access points 210A, 210B can communicate with the network220 via one or more types of wired or wireless communication links 230A,230B. Although FIG. 2 shows the computing and communication devices100A, 100B, 100C in communication via the network 220, the computing andcommunication devices 100A, 100B, 100C can communicate with each othervia any number of communication links, such as a direct wired orwireless communication link.

In some implementations, communications between one or more of thecomputing and communication device 100A, 100B, 100C may omitcommunicating via the network 220 and may include transferring data viaanother medium (not shown), such as a data storage device. For example,the server computing and communication device 100C may store audio data,such as encoded audio data, in a data storage device, such as a portabledata storage unit, and one or both of the computing and communicationdevice 100A or the computing and communication device 100B may access,read, or retrieve the stored audio data from the data storage unit, suchas by physically disconnecting the data storage device from the servercomputing and communication device 100C and physically connecting thedata storage device to the computing and communication device 100A orthe computing and communication device 100B.

Other implementations of the computing and communications system 200 arepossible. For example, in an implementation, the network 220 can be anad-hoc network and can omit one or more of the access points 210A, 210B.The computing and communications system 200 may include devices, units,or elements not shown in FIG. 2 . For example, the computing andcommunications system 200 may include many more communicating devices,networks, and access points.

FIG. 3 is a diagram of a video stream 300 for use in encoding anddecoding in accordance with implementations of this disclosure. A videostream 300, such as a video stream captured by a video camera or a videostream generated by a computing device, may include a video sequence310. The video sequence 310 may include a sequence of adjacent frames320. Although three adjacent frames 320 are shown, the video sequence310 can include any number of adjacent frames 320.

Each frame 330 from the adjacent frames 320 may represent a single imagefrom the video stream. Although not shown in FIG. 3 , a frame 330 mayinclude one or more segments, tiles, or planes, which may be coded, orotherwise processed, independently, such as in parallel. A frame 330 mayinclude one or more tiles 340. Each of the tiles 340 may be arectangular region of the frame that can be coded independently. Each ofthe tiles 340 may include respective blocks 350. Although not shown inFIG. 3 , a block can include pixels. For example, a block can include a16×16 group of pixels, an 8×8 group of pixels, an 8×16 group of pixels,or any other group of pixels. Unless otherwise indicated herein, theterm ‘block’ can include a superblock, a macroblock, a segment, a slice,or any other portion of a frame. A frame, a block, a pixel, or acombination thereof can include display information, such as luminanceinformation, chrominance information, or any other information that canbe used to store, modify, communicate, or display the video stream or aportion thereof.

FIG. 4 is a block diagram of an encoder 400 in accordance withimplementations of this disclosure. Encoder 400 can be implemented in adevice, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2 ,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1 . The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1 , and may cause thedevice to encode video data as described herein. The encoder 400 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The encoder 400 can encode an input video stream 402, such as the videostream 300 shown in FIG. 3 , to generate an encoded (compressed)bitstream 404. In some implementations, the encoder 400 may include aforward path for generating the compressed bitstream 404. The forwardpath may include an intra/inter prediction unit 410, a transform unit420, a quantization unit 430, an entropy encoding unit 440, or anycombination thereof. In some implementations, the encoder 400 mayinclude a reconstruction path (indicated by the broken connection lines)to reconstruct a frame for encoding of further blocks. Thereconstruction path may include a dequantization unit 450, an inversetransform unit 460, a reconstruction unit 470, a filtering unit 480, orany combination thereof. Other structural variations of the encoder 400can be used to encode the video stream 402.

For encoding the video stream 402, each frame within the video stream402 can be processed in units of blocks. Thus, a current block may beidentified from the blocks in a frame, and the current block may beencoded.

At the intra/inter prediction unit 410, the current block can be encodedusing either intra-frame prediction, which may be within a single frame,or inter-frame prediction, which may be from frame to frame.Intra-prediction may include generating a prediction block from samplesin the current frame that have been previously encoded andreconstructed. Inter-prediction may include generating a predictionblock from samples in one or more previously constructed referenceframes. Generating a prediction block for a current block in a currentframe may include performing motion estimation to generate a motionvector indicating an appropriate reference portion of the referenceframe.

The intra/inter prediction unit 410 may subtract the prediction blockfrom the current block (raw block) to produce a residual block. Thetransform unit 420 may perform a block-based transform, which mayinclude transforming the residual block into transform coefficients in,for example, the frequency domain. Examples of block-based transformsinclude the Karhunen-Loève Transform (KLT), the Discrete CosineTransform (DCT), the Singular Value Decomposition Transform (SVD), andthe Asymmetric Discrete Sine Transform (ADST). In an example, the DCTmay include transforming a block into the frequency domain. The DCT mayinclude using transform coefficient values based on spatial frequency,with the lowest frequency (i.e. DC) coefficient at the top-left of thematrix and the highest frequency coefficient at the bottom-right of thematrix.

The quantization unit 430 may convert the transform coefficients intodiscrete quantum values, which may be referred to as quantized transformcoefficients or quantization levels. The quantized transformcoefficients can be entropy encoded by the entropy encoding unit 440 toproduce entropy-encoded coefficients. Entropy encoding can include usinga probability distribution metric. The entropy-encoded coefficients andinformation used to decode the block, which may include the type ofprediction used, motion vectors, and quantizer values, can be output tothe compressed bitstream 404. The compressed bitstream 404 can beformatted using various techniques, such as run-length encoding (RLE)and zero-run coding.

The reconstruction path can be used to maintain reference framesynchronization between the encoder 400 and a corresponding decoder,such as the decoder 500 shown in FIG. 5 . The reconstruction path may besimilar to the decoding process discussed below and may include decodingthe encoded frame, or a portion thereof, which may include decoding anencoded block, which may include dequantizing the quantized transformcoefficients at the dequantization unit 450 and inverse transforming thedequantized transform coefficients at the inverse transform unit 460 toproduce a derivative residual block. The reconstruction unit 470 may addthe prediction block generated by the intra/inter prediction unit 410 tothe derivative residual block to create a decoded block. The filteringunit 480 can be applied to the decoded block to generate a reconstructedblock, which may reduce distortion, such as blocking artifacts. Althoughone filtering unit 480 is shown in FIG. 4 , filtering the decoded blockmay include loop filtering, deblocking filtering, or other types offiltering or combinations of types of filtering. The reconstructed blockmay be stored or otherwise made accessible as a reconstructed block,which may be a portion of a reference frame, for encoding anotherportion of the current frame, another frame, or both, as indicated bythe broken line at 482. Coding information, such as deblocking thresholdindex values, for the frame may be encoded, included in the compressedbitstream 404, or both, as indicated by the broken line at 484.

Other variations of the encoder 400 can be used to encode the compressedbitstream 404. For example, a non-transform-based encoder 400 canquantize the residual block directly without the transform unit 420. Insome implementations, the quantization unit 430 and the dequantizationunit 450 may be combined into a single unit.

FIG. 5 is a block diagram of a decoder 500 in accordance withimplementations of this disclosure. The decoder 500 can be implementedin a device, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2 ,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1 . The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1 , and may cause thedevice to decode video data as described herein. The decoder 500 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The decoder 500 may receive a compressed bitstream 502, such as thecompressed bitstream 404 shown in FIG. 4 , and may decode the compressedbitstream 502 to generate an output video stream 504. The decoder 500may include an entropy decoding unit 510, a dequantization unit 520, aninverse transform unit 530, an intra/inter prediction unit 540, areconstruction unit 550, a filtering unit 560, or any combinationthereof. Other structural variations of the decoder 500 can be used todecode the compressed bitstream 502.

The entropy decoding unit 510 may decode data elements within thecompressed bitstream 502 using, for example, Context Adaptive BinaryArithmetic Decoding, to produce a set of quantized transformcoefficients. The dequantization unit 520 can dequantize the quantizedtransform coefficients, and the inverse transform unit 530 can inversetransform the dequantized transform coefficients to produce a derivativeresidual block, which may correspond to the derivative residual blockgenerated by the inverse transform unit 460 shown in FIG. 4 . Usingheader information decoded from the compressed bitstream 502, theintra/inter prediction unit 540 may generate a prediction blockcorresponding to the prediction block created in the encoder 400. At thereconstruction unit 550, the prediction block can be added to thederivative residual block to create a decoded block. The filtering unit560 can be applied to the decoded block to reduce artifacts, such asblocking artifacts, which may include loop filtering, deblockingfiltering, or other types of filtering or combinations of types offiltering, and which may include generating a reconstructed block, whichmay be output as the output video stream 504.

Other variations of the decoder 500 can be used to decode the compressedbitstream 502. For example, the decoder 500 can produce the output videostream 504 without the filtering unit 560.

FIG. 6 is a block diagram of a representation of a portion 600 of aframe, such as the frame 330 shown in FIG. 3 , in accordance withimplementations of this disclosure. As shown, the portion 600 of theframe includes four 64×64 blocks 610, in two rows and two columns in amatrix or Cartesian plane. In some implementations, a 64×64 block may bea maximum coding unit, N=64. Each 64×64 block may include four 32×32blocks 620. Each 32×32 block may include four 16×16 blocks 630. Each16×16 block may include four 8×8 blocks 640. Each 8×8 block 640 mayinclude four 4×4 blocks 650. Each 4×4 block 650 may include 16 pixels,which may be represented in four rows and four columns in eachrespective block in the Cartesian plane or matrix. The pixels mayinclude information representing an image captured in the frame, such asluminance information, color information, and location information. Insome implementations, a block, such as a 16×16 pixel block as shown, mayinclude a luminance block 660, which may include luminance pixels 662;and two chrominance blocks 670, 680, such as a U or Cb chrominance block670, and a V or Cr chrominance block 680. The chrominance blocks 670,680 may include chrominance pixels 690. For example, the luminance block660 may include 16×16 luminance pixels 662 and each chrominance block670, 680 may include 8×8 chrominance pixels 690 as shown. Although onearrangement of blocks is shown, any arrangement may be used. AlthoughFIG. 6 shows N×N blocks, in some implementations, N×M blocks may beused. For example, 32×64 blocks, 64×32 blocks, 16×32 blocks, 32×16blocks, or any other size blocks may be used. In some implementations,N×2N blocks, 2N×N blocks, or a combination thereof may be used.

In some implementations, video coding may include ordered block-levelcoding. Ordered block-level coding may include coding blocks of a framein an order, such as raster-scan order, wherein blocks may be identifiedand processed starting with a block in the upper left corner of theframe, or portion of the frame, and proceeding along rows from left toright and from the top row to the bottom row, identifying each block inturn for processing. For example, the 64×64 block in the top row andleft column of a frame may be the first block coded and the 64×64 blockimmediately to the right of the first block may be the second blockcoded. The second row from the top may be the second row coded, suchthat the 64×64 block in the left column of the second row may be codedafter the 64×64 block in the rightmost column of the first row.

In some implementations, coding a block may include using quad-treecoding, which may include coding smaller block units within a block inraster-scan order. For example, the 64×64 block shown in the bottom leftcorner of the portion of the frame shown in FIG. 6 , may be coded usingquad-tree coding wherein the top left 32×32 block may be coded, then thetop right 32×32 block may be coded, then the bottom left 32×32 block maybe coded, and then the bottom right 32×32 block may be coded. Each 32×32block may be coded using quad-tree coding wherein the top left 16×16block may be coded, then the top right 16×16 block may be coded, thenthe bottom left 16×16 block may be coded, and then the bottom right16×16 block may be coded. Each 16×16 block may be coded using quad-treecoding wherein the top left 8×8 block may be coded, then the top right8×8 block may be coded, then the bottom left 8×8 block may be coded, andthen the bottom right 8×8 block may be coded. Each 8×8 block may becoded using quad-tree coding wherein the top left 4×4 block may becoded, then the top right 4×4 block may be coded, then the bottom left4×4 block may be coded, and then the bottom right 4×4 block may becoded. In some implementations, 8×8 blocks may be omitted for a 16×16block, and the 16×16 block may be coded using quad-tree coding whereinthe top left 4×4 block may be coded, then the other 4×4 blocks in the16×16 block may be coded in raster-scan order.

In some implementations, video coding may include compressing theinformation included in an original, or input, frame by, for example,omitting some of the information in the original frame from acorresponding encoded frame. For example, coding may include reducingspectral redundancy, reducing spatial redundancy, reducing temporalredundancy, or a combination thereof.

In some implementations, reducing spectral redundancy may include usinga color model based on a luminance component (Y) and two chrominancecomponents (U and V or Cb and Cr), which may be referred to as the YUVor YCbCr color model, or color space. Using the YUV color model mayinclude using a relatively large amount of information to represent theluminance component of a portion of a frame and using a relatively smallamount of information to represent each corresponding chrominancecomponent for the portion of the frame. For example, a portion of aframe may be represented by a high-resolution luminance component, whichmay include a 16×16 block of pixels, and by two lower resolutionchrominance components, each of which represents the portion of theframe as an 8×8 block of pixels. A pixel may indicate a value, forexample, a value in the range from 0 to 255, and may be stored ortransmitted using, for example, eight bits. Although this disclosure isdescribed in reference to the YUV color model, any color model may beused.

In some implementations, reducing spatial redundancy may includetransforming a block into the frequency domain using, for example, adiscrete cosine transform (DCT). For example, a unit of an encoder, suchas the transform unit 420 shown in FIG. 4 , may perform a DCT usingtransform coefficient values based on spatial frequency.

In some implementations, reducing temporal redundancy may include usingsimilarities between frames to encode a frame using a relatively smallamount of data based on one or more reference frames, which may bepreviously encoded, decoded, and reconstructed frames of the videostream. For example, a block or pixel of a current frame may be similarto a spatially corresponding block or pixel of a reference frame. Insome implementations, a block or pixel of a current frame may be similarto block or pixel of a reference frame at a different spatial locationand reducing temporal redundancy may include generating motioninformation indicating the spatial difference, or translation, betweenthe location of the block or pixel in the current frame andcorresponding location of the block or pixel in the reference frame.

In some implementations, reducing temporal redundancy may includeidentifying a portion of a reference frame that corresponds to a currentblock or pixel of a current frame. For example, a reference frame, or aportion of a reference frame, which may be stored in memory, may besearched to identify a portion for generating a prediction to use forencoding a current block or pixel of the current frame with maximalefficiency. For example, the search may identify a portion of thereference frame for which the difference in pixel values between thecurrent block and a prediction block generated based on the portion ofthe reference frame is minimized and may be referred to as motionsearching. In some implementations, the portion of the reference framesearched may be limited. For example, the portion of the reference framesearched, which may be referred to as the search area, may include alimited number of rows of the reference frame. In an example,identifying the portion of the reference frame for generating aprediction may include calculating a cost function, such as a sum ofabsolute differences (SAD), between the pixels of portions of the searcharea and the pixels of the current block.

In some implementations, the spatial difference between the location ofthe portion of the reference frame for generating a prediction in thereference frame and the current block in the current frame may berepresented as a motion vector. The difference in pixel values betweenthe prediction block and the current block may be referred to asdifferential data, residual data, a prediction error, or as a residualblock. In some implementations, generating motion vectors may bereferred to as motion estimation, and a pixel of a current block may beindicated based on location using Cartesian coordinates as f_(x,y).Similarly, a pixel of the search area of the reference frame may beindicated based on location using Cartesian coordinates as r_(x,y). Amotion vector (MV) for the current block may be determined based on, forexample, a SAD between the pixels of the current frame and thecorresponding pixels of the reference frame.

Although described herein with reference to matrix or Cartesianrepresentation of a frame for clarity, a frame may be stored,transmitted, processed, or any combination thereof, in any datastructure such that pixel values may be efficiently represented for aframe or image. For example, a frame may be stored, transmitted,processed, or any combination thereof, in a two-dimensional datastructure such as a matrix as shown, or in a one-dimensional datastructure, such as a vector array. In an implementation, arepresentation of the frame, such as a two-dimensional representation asshown, may correspond to a physical location in a rendering of the frameas an image. For example, a location in the top left corner of a blockin the top left corner of the frame may correspond with a physicallocation in the top left corner of a rendering of the frame as an image.

In some implementations, block-based coding efficiency may be improvedby partitioning input blocks into one or more prediction partitions,which may be rectangular, including square, partitions for predictioncoding. In some implementations, video coding using predictionpartitioning may include selecting a prediction partitioning scheme fromamong multiple candidate prediction partitioning schemes. For example,in some implementations, candidate prediction partitioning schemes for a64×64 coding unit may include rectangular size prediction partitionsranging in sizes from 4×4 to 64×64, such as 4×4, 4×8, 8×4, 8×8, 8×16,16×8, 16×16, 16×32, 32×16, 32×32, 32×64, 64×32, or 64×64. In someimplementations, video coding using prediction partitioning may includea full prediction partition search, which may include selecting aprediction partitioning scheme by encoding the coding unit using eachavailable candidate prediction partitioning scheme and selecting thebest scheme, such as the scheme that produces the least rate-distortionerror.

In some implementations, encoding a video frame may include identifyinga prediction partitioning scheme for encoding a current block, such asblock 610. In some implementations, identifying a predictionpartitioning scheme may include determining whether to encode the blockas a single prediction partition of maximum coding unit size, which maybe 64×64 as shown, or to partition the block into multiple predictionpartitions, which may correspond with the sub-blocks, such as the 32×32blocks 620 the 16×16 blocks 630, or the 8×8 blocks 640, as shown, andmay include determining whether to partition into one or more smallerprediction partitions. For example, a 64×64 block may be partitionedinto four 32×32 prediction partitions. Three of the four 32×32prediction partitions may be encoded as 32×32 prediction partitions andthe fourth 32×32 prediction partition may be further partitioned intofour 16×16 prediction partitions. Three of the four 16×16 predictionpartitions may be encoded as 16×16 prediction partitions and the fourth16×16 prediction partition may be further partitioned into four 8×8prediction partitions, each of which may be encoded as an 8×8 predictionpartition. In some implementations, identifying the predictionpartitioning scheme may include using a prediction partitioning decisiontree.

In some implementations, video coding for a current block may includeidentifying an optimal prediction coding mode from multiple candidateprediction coding modes, which may provide flexibility in handling videosignals with various statistical properties and may improve thecompression efficiency. For example, a video coder may evaluate eachcandidate prediction coding mode to identify the optimal predictioncoding mode, which may be, for example, the prediction coding mode thatminimizes an error metric, such as a rate-distortion cost, for thecurrent block. In some implementations, the complexity of searching thecandidate prediction coding modes may be reduced by limiting the set ofavailable candidate prediction coding modes based on similaritiesbetween the current block and a corresponding prediction block. In someimplementations, the complexity of searching each candidate predictioncoding mode may be reduced by performing a directed refinement modesearch. For example, metrics may be generated for a limited set ofcandidate block sizes, such as 16×16, 8×8, and 4×4, the error metricassociated with each block size may be in descending order, andadditional candidate block sizes, such as 4×8 and 8×4 block sizes, maybe evaluated.

In some implementations, block-based coding efficiency may be improvedby partitioning a current residual block into one or more transformpartitions, which may be rectangular, including square, partitions fortransform coding. In some implementations, video coding using transformpartitioning may include selecting a uniform transform partitioningscheme. For example, a current residual block, such as block 610, may bea 64×64 block and may be transformed without partitioning using a 64×64transform.

Although not expressly shown in FIG. 6 , a residual block may betransform partitioned using a uniform transform partitioning scheme. Forexample, a 64×64 residual block may be transform partitioned using auniform transform partitioning scheme including four 32×32 transformblocks, using a uniform transform partitioning scheme including sixteen16×16 transform blocks, using a uniform transform partitioning schemeincluding sixty-four 8×8 transform blocks, or using a uniform transformpartitioning scheme including 256 4×4 transform blocks.

In some implementations, video coding using transform partitioning mayinclude identifying multiple transform block sizes for a residual blockusing multiform transform partition coding. In some implementations,multiform transform partition coding may include recursively determiningwhether to transform a current block using a current block sizetransform or by partitioning the current block and multiform transformpartition coding each partition. For example, the bottom left block 610shown in FIG. 6 may be a 64×64 residual block, and multiform transformpartition coding may include determining whether to code the current64×64 residual block using a 64×64 transform or to code the 64×64residual block by partitioning the 64×64 residual block into partitions,such as four 32×32 blocks 620, and multiform transform partition codingeach partition. In some implementations, determining whether totransform partition the current block may be based on comparing a costfor encoding the current block using a current block size transform to asum of costs for encoding each partition using partition sizetransforms.

FIG. 7 is a flowchart diagram of an example of encoding usingmultiresolution quantization interval information 700 in accordance withimplementations of this disclosure. Encoding using multiresolutionquantization interval information 700 may be implemented in an encoder,such as the encoder 400 shown in FIG. 4 . Although the encoder 400 shownin FIG. 4 and the decoder 500 shown in FIG. 5 are described with respectto video coding, encoding using multiresolution quantization intervalinformation 700 may be implemented for still image coding, video coding,or both.

As shown in FIG. 7 , encoding using multiresolution quantizationinterval information 700 includes obtaining an input image at 710,identifying a current block at 720, identifying sub-block DC values at730, determining quantization interval information for the current blockat 740, and outputting at 750.

An input image may be obtained at 710. The input image may be anuncompressed input, or source, image or video frame. For example, theencoder may receive, or otherwise access, an input image or input videostream or signal, or a portion thereof, and may identify the input imageor a portion of the input video stream as the current input image.Identifying an input image may include receiving one or more inputimages at a buffer and buffering the input images.

The current block may be identified at 720. Identifying the currentblock may include identifying the current block from the input imageidentified at 710. For example, the current block may be a block, suchas block 630 shown in FIG. 6 . The current block may be identifiedaccording to a block scan order. In some implementations, identifyingthe current block at 720 may include identifying a tile from the inputimage and identifying the current block from the tile. Although currentframe is described with reference to forward raster scan order, anyblock scan order may be used.

The current block identified at 720 may be a 2N×2N block that includestwo or more sub-blocks, such as N×N sub-blocks. The current blockidentified at 720 may include one or more transform blocks. In anexample, the current image may be divided into transform 8×8 blocks andidentifying the current block may include identifying a 16×16 blockincluding four 8×8 transform blocks (sub-blocks). The current blockidentified at 720 may include, in the frequency domain, direct component(DC) information, such as a top-left transform coefficient of atransform block, on a block basis for blocks having a defined size(block resolution), such as 8×8.

Sub-block DC values may be identified at 730. Identifying the sub-blockDC values may include identifying a top-left transform coefficient fromeach of the N×N sub-blocks of the 2N×2N current block. In someimplementations, the 2N×2N current block may spatially overlap atransform block having a transform block size other than N×N, such as2N×N, N×2N, 2N×2N, or larger, and pseudo-DC values corresponding to N×Ntransform blocks may be determined. For example, a transform block forencoding the current image may have a size of N×N, and the DC value forthe transform block may be used. In another example, a transform blockfor encoding the current image may have a size larger than N×N in atleast one dimension, and pseudo-DC values for each N×N portion oftransform block may be used. Determining a pseudo-DC value may besimilar to determining a DC value for the portion of the image, exceptthat the image may be coded using a transform size larger than theportion corresponding to the pseudo-DC value. In an example, a portionof the current image may be coded using an 8×16 transform and a firstpseudo-DC value may be determined for a first 8×8 portion of the imagespatially concurrent with a first portion of the 8×16 transform and asecond pseudo-DC value may be determined for a second 8×8 portion of theimage spatially concurrent with a second portion of the 8×16 transform.In some implementations, the current block may have a defined minimumquantization interval information block size, such as 8×8, andidentifying the sub-block DC values may be omitted. The DC values may beidentified on a per-channel basis. For example, in an RGB format, threeDC values, one for each channel, may be identified for each sub-block.

Quantization interval information may be determined for the currentblock at 740. The quantization interval information may indicate whetherthe DC values identified at 730 are above a quantization boundary(within the top half of a corresponding quantization range), such asgreater than the quantization boundary, or are below the quantizationboundary (within the bottom half of the corresponding quantizationrange), such as less than or equal to the quantization boundary. Forexample, a quantization interval information value of zero (0) mayindicate that the DC values are in the bottom half of the correspondingquantization range, range minimum<=DC<=quantization boundary, and aquantization interval information value of one (1) may indicate that theDC values are in the top half of the corresponding quantization range,quantization boundary<DC<=range maximum. In some implementations, DCvalues that are equal to the quantization boundary may be identified asabove the quantization boundary corresponding to a quantization intervalinformation value of one (1).

In some implementations, the current block may have the minimumquantization interval information block size, such as 8×8, which may bethe minimum transform block size, and the quantization intervalinformation may be determined for the current block based on the DCvalue for the current block. For example, the DC value for an 8×8transform block may be 19.75, quantization boundary may be 20, thecorresponding quantization range may be from 19.5 to 20.5, and thequantization interval information value for the block may be identifiedas zero.

Determining the quantization interval information may includedetermining multiresolution quantization interval information. Forexample, quantization interval information may be determining formultiple block sizes, such as 8×8, 16×16, 32×32, and 64×64. Other blocksizes may be used. The quantization boundary and quantization range maycorrespond with the block size, or resolution. For example, thequantization boundary for a 16×16 block may be 80 and the correspondingrange may be 78 to 82.

In some implementations, the current block may be a 2N×2N block,sub-block DC values, such as sub-block DC values corresponding to fourN×N sub-blocks may be identified for the current block at 730, a sum ofthe sub-block DC values may be determined, and the quantization intervalinformation value may be determined based on whether the sum of thesub-block DC values is within the top half or the bottom half of thequantization range corresponding to the 2N×2N block.

For example, the current block may be a 16×16 block, the DC value for afirst 8×8 transform block may be 19.75, the DC value for a second 8×8transform block may be 20.25, the DC value for a third 8×8 transformblock may be 20.75, the DC value for a fourth 8×8 transform block may be20.75, the sum of the DC values for the sub-blocks may be 81.5, thequantization boundary corresponding to the 16×16 block may be 82, thesum of the DC values for the sub-blocks may be in the bottom half of thequantization range, and the quantization interval information value maybe identified as zero (0).

In some implementations, determining the quantization intervalinformation at 740 may include determining whether to omit thequantization interval information for a current block, such as based ona smoothness constraint, such as a maximum variance constraint. Forexample, the maximum variance among the DC values for the current blockmay be within a defined maximum variance threshold, such as within, suchas equal to or less than, three quantization step sizes, and thequantization interval information for the current block may bedetermined at 740 and included for the current block. In another examplethe maximum variance among the DC values for the current block may begreater than the defined maximum variance threshold, such as greaterthan three quantization step sizes, and determining the quantizationinterval information for the current block may be omitted at 740 andquantization interval information for the current block may be omittedfrom outputting at 750.

The quantization interval information may be identified on a per-channelbasis. For example, in an RGB format, three quantization intervalinformation values, one for each channel, may be identified for eachblock. Although not shown expressly in FIG. 7 , identifying the currentblock at 720, identifying sub-block DC values at 730, and determiningquantization interval information for the current block at 740 may beperformed for each (non-overlapping) block in the frame at the currentblock size (2N×2N). For example, the current block size (2N×2N) may be8×8, and quantization interval information may be identified for each8×8 block of the frame.

Whether the current block size is less than the maximum block size (orresolution) for determining quantization interval information may bedetermined at 745. For example, the current block size (2N×2N) may be8×8, the maximum block size for determining quantization intervalinformation may be 64×64, and it may be determined that the currentblock size is less than the maximum block size for determiningquantization interval information.

In response to a determination that the current block size is equal toor greater than the maximum block size for determining quantizationinterval information, the quantization interval information may beoutput at 750. In response to a determination that the current blocksize is less than the maximum block size for determining quantizationinterval information, the current block size may be increased andidentifying the current block at 720, identifying sub-block DC values at730, and determining quantization interval information for the currentblock at 740 may be performed for each (non-overlapping) block in theframe at the increased current block size, as indicated by the brokendirection line from 745 to 720.

For example, the current block size may be 8×8 and quantization intervalinformation may be determined for each 8×8 block in the frame (720-740for each 8×8 block), the current block size may be increased to 16×16 at745 and quantization interval information may be determined for each16×16 block in the frame (720-740 for each 16×16 block), the currentblock size may be increased to 32×32 at 745 and quantization intervalinformation may be determined for each 32×32 block in the frame (720-740for each 32×32 block), the current block size may be increased to 64×64at 745 and quantization interval information may be determined for each64×64 block in the frame (720-740 for each 64×64 block), the currentblock size of 64×64 may be determined to be equal to the maximum blocksize for determining quantization interval information at 745, and inresponse to the determination that the current block size of 64×64 isequal to or greater than the maximum block size for determiningquantization interval information of 64×64, the quantization intervalinformation may be output at 750.

The quantization interval information may be output at 750. Outputtingthe quantization interval information may include including thequantization interval information, such as the quantization intervalinformation values for each channel, for each block, for each block size(or resolution) in an encoded output such as in a respective header foreach respective block, such as an encoded bitstream, representing theencoded image. The quantization interval information for the image mayinclude quantization interval information for each 8×8 portion of theimage, each 16×16 portion of the image, each 32×32 portion of the image,and each 64×64 portion of the image. Although not shown expressly inFIG. 7 , the transform coefficients may be quantized, and the quantizedtransform coefficients may be included in the output. Quantizing thetransform coefficients may introduce quantization error (mach banding).The quantization interval information included in the encoded image maybe used by the decoder to reduce the quantization error. For example,including the quantization interval information for 8×8 blocks, andomitting quantization interval information for larger blocks may resultin a quantization error of plus or minus 2 values for a spatiallycorresponding 16×16 block. Including one bit of quantization intervalinformation for the 16×16 block may reduce the quantization error forthe 16×16 block to plus or minus one value.

Other implementations of encoding using multiresolution quantizationinterval information 700 are available. For example, the maximum blocksize may be 128×128. In another example, two or more bits per channel,per block, for each block size greater than the minimum block size maybe included to further reduce the maximum error and correspondingquantization error (mach banding). For example, including two bits ofquantization interval information for a 2N×2N block may reduce thequantization error for the 2N×2N block to plus or minus one half of onevalue. In some implementations, additional elements of encoding usingmultiresolution quantization interval information can be added, certainelements can be combined, and/or certain elements can be removed.

FIG. 8 is a flowchart diagram of an example of quantization constrainedneural image coding 800 in accordance with implementations of thisdisclosure. Quantization constrained neural image coding 800 may beimplemented in an encoder, such as in the decode path of the encoder 400shown in FIG. 4 , or a decoder, such as the decoder 500 shown in FIG. 5. Although the encoder 400 shown in FIG. 4 and the decoder 500 shown inFIG. 5 are described with respect to video coding, quantizationconstrained neural image coding 800 may be implemented for still imagecoding, video coding, or both.

As shown in FIG. 8 , quantization constrained neural image coding 800includes obtaining a source image at 810, outputting the source image at820, generating a restoration filtered image at 830, generating aquantization constrained restoration filtered image at 840, outputtingthe quantization constrained restoration filtered image at 850,generating an artificial neural network improved image at 860,generating a quantization constrained artificial neural network improvedimage at 870, and outputting the quantization constrained artificialneural network improved image at 880.

The source image may be obtained at 810. For example, the source imagemay be obtained in a defined format, such as the JPEG format, having adefined color model, such as an additive color model, such as RGB, whichmay include a red color channel, a green color channel, and a blue colorchannel. The source image may be a relatively low-quality image, whichmay have a relatively low bit-rate. For example, the source image mayinclude one or more coding artifacts such as quantization banding (machbanding), tiling, or ringing. Obtaining the source image at 810 mayinclude identifying quantization information, such as one or morequantization parameters, based on the source image. For example, thesource image may include a quantization table including quantizationcoefficients. In some implementations, the source image may be obtainedin a format similar to the JPEG format, except that the image mayinclude multiresolution quantization interval information, such asmultiresolution quantization interval information generated as shown inFIG. 7 , and obtaining the quantization information may includeobtaining the multiresolution quantization interval information. Themultiresolution quantization interval information may include respectivequantization interval information for two or more block sizes(resolutions) from a defined set of block sizes, such as 8×8, 16×16,32×32, and 64×64. The quantization interval information for a block sizemay be a bit or a flag and may indicate whether the top-left transformcoefficient (DC value) for the block is within the upper or lowerportion of the corresponding quantization interval or range.

Obtaining the source image at 810 may include obtaining an encoded imageand reconstructing the source image by decoding the encoded image.Obtaining the source image at 810 may include obtaining quantizedtransform coefficients and dequantizing the quantized transformcoefficients to obtain transform coefficients corresponding torespective transform blocks. In some implementations, such as forrelatively low-quality source images, the global color accuracy of thesource image may be low. For example, the source image may be areconstruction of an encoded or compressed image that may be generatedby encoding or compressing a source image using high-compression rate,low image quality encoding or compression.

The source image may be output at 820. For example, the source imageobtained at 810 may be output for presentation to a user via displaydevice. The reconstructed source image output at 820 may includeartifacts, such as mach banding, blocking, ringing, or a combinationthereof. Outputting the source image at 820 may be omitted, as indicatedby the broken line border at 820.

A restoration filtered image may be generated at 830. Generating therestoration filtered image may include restoration filtering the sourceimage obtained at 810 using one or more restoration filters. Forexamples, the restoration filtering may include smoothing, such asselective Gaussian smoothing, or other regularization. The restorationfiltered image may be a relatively high-quality image relative to thesource image. For example, the restoration filtering may reduce oreliminate tiling (or blocking) artifacts, ringing artifacts, or bothrelative to the source image. For example, restoration filtering mayinclude deblocking. The restoration filtering may be based on the sourceimage, which may include the quantization interval information.

The restoration filtered image generated at 830 may differ from thesource image obtained at 810. One or more portions of the restorationfiltered image generated at 830 may include one or more values outsiderespective value ranges indicated by the quantization informationidentified for the source image at 810. For example, a quantized imagegenerated by quantizing the restoration filtered image generated at 830based on the quantization information identified for the source image at810 may differ from the quantized source image obtained at 810.

A quantization constrained restoration filtered image may be generatedat 840. Generating the quantization constrained restoration filteredimage generated at 840 may include constraining, such as limiting,truncating, or rounding, values of the restoration filtered imagegenerated at 830 based on the quantization information identified forthe source image at 810.

The quantization constrained restoration filtered image generated at 840may differ from the quantized source image obtained at 810. A quantizedimage generated by quantizing the quantization constrained restorationfiltered image generated at 840 based on the quantization informationidentified for the source image at 810 may be equivalent to the sourceimage obtained at 810. For example, the difference between the quantizedimage generated by quantizing the quantization constrained restorationfiltered image generated at 840 based on the quantization informationidentified for the source image at 810 and the source image obtained at810 may be within a defined similarity threshold, such as zero.

Generating a restoration filtered image at 830 and generating aquantization constrained restoration filtered image at 840 may beserially repeated, as indicated by the broken directional line at 845,such as one, two, or three times, wherein each iteration improves thequality, such as by removing or reducing artifacts, of the generatedimages relative to a previous iteration. Although shown separately inFIG. 8 , generating the restoration filtered image at 830 and generatingthe quantization constrained restoration filtered image at 840 may becombined.

The quantization constrained restoration filtered image may be output at850. For example, the quantization constrained restoration filteredimage generated at 840 may be output for presentation to a user viadisplay device at 850. Outputting the quantization constrainedrestoration filtered image at 850 may include outputting thequantization constrained restoration filtered image such that apresentation of the source image output at 820 is replaced by apresentation of the quantization constrained restoration filtered imageoutput at 850. Outputting the quantization constrained restorationfiltered image at 850 may be omitted, as indicated by the broken lineborder at 850.

An artificial neural network improved image (artificial image) may begenerated at 860. Generating an artificial image may include using anartificial neural network (ANN), such as a deep learning neural network,a convolution of neural networks, or other machine learning technique,to generate the artificial image based on the quantization constrainedrestoration filtered image generated at 840. For example, the artificialneural network may be a generative artificial neural network of aconditional generative adversarial network (GAN). An artificial imagemay include a combination of image detail from an original or sourceimage and artificial image detail generated by an artificial neuralnetwork.

The generative adversarial network may include the generative artificialneural network and a discriminative artificial neural network. Thegenerative artificial neural network may generate one or more artificialimages, such as based on input data, such as a source image, such as thequantization constrained restoration filtered image generated at 840.The discriminative artificial neural network may label an image as anauthentic image or an artificial image. The authentic images may beimages previously identified as authentic images.

The generative artificial neural network may be trained to improve theimage quality of artificial images based on the image labels output bythe discriminative artificial neural network (feedback from thediscriminative artificial neural network), wherein training thegenerative artificial neural network corresponds to increasing theprobability that an artificial image generated by the generativeartificial neural network is labeled by the discriminative artificialneural network as an authentic image.

The discriminative artificial neural network may be trained to improvethe probability of accurately labeling authentic images as authenticimages and accurately labeling artificial images as artificial images,wherein information indicating the accuracy of the labeling of theartificial images may be used as feedback to the discriminativeartificial neural network.

The generative artificial neural network and the discriminativeartificial neural network may be trained in combination, such as in agenerative adversarial network. Training the generative artificialneural network and the discriminative artificial neural network mayinclude identifying complexity targets and training the generativeartificial neural networks, the discriminative artificial neuralnetworks, or both based on the target complexities. For example, a firstgenerative artificial neural network may be generated for a firstcomplexity target, a second generative artificial neural network may begenerated for a second complexity target, and a third generativeartificial neural network may be generated for a third complexitytarget. The complexity target may represent a model size or complexity,such as a cardinality of layers, a cardinality of artificial neurons, orboth. The complexity target may correspond with a target set ofcapabilities or operating conditions of the encoder, decoder, or both,implementing quantization constrained neural image coding 800. Forexample, the first complexity target may correspond with a first decoderhaving relatively limited capabilities, the second complexity target maycorrespond with a second decoder having capabilities greater than thefirst decoder, the third complexity target may correspond with a thirddecoder having capabilities greater than the second decoder, such thatthe first generative artificial neural network may be relatively simplehaving relatively few layers, artificial neurons, or both. The secondgenerative artificial neural network may be more complex than the firstgenerative artificial neural network and may have more layers,artificial neurons, or both, than the first generative artificial neuralnetwork. The third generative artificial neural network may be morecomplex than the second generative artificial neural network and mayhave more layers, artificial neurons, or both, than the secondgenerative artificial neural network. The complexity targets may bedefined values, which may be adjustable to balance decoding time againstimage quality, where lower complexity corresponds with lower decodingtime and lower image quality and higher complexity corresponds withhigher decoding time and higher image quality.

The artificial image may be a relatively high-quality image relative tothe quantization constrained restoration filtered image generated at840. For example, the artificial image may have increased material ortexture detail or quality relative to the quantization constrainedrestoration filtered image generated at 840. The difference between theartificial image and the corresponding source image may be image detailomitted from the source image, which may be generated based on imageinformation included in the input to the generative artificial neuralnetwork, such as based on the source image or the quantizationconstrained restoration filtered image.

The artificial image generated at 860 may differ from the quantizedsource image obtained at 810. One or more portions of the artificialimage generated at 860 may include one or more values outside respectivevalue ranges indicated by the quantization information identified forthe source image at 810. For example, a quantized image generated byquantizing the artificial image generated at 860 based on thequantization information identified for the source image at 810 maydiffer from the quantized source image obtained at 810. Differencesbetween the quantized image generated by quantizing the artificial imageand the source image may correspond to artificial image detailartifacts.

In some implementations, outputting the source image at 820, generatingthe restoration filtered image at 830, generating the quantizationconstrained restoration filtered image at 840, and outputting thequantization constrained restoration filtered image at 850 may beomitted and generating an artificial neural network improved image at860 may be performed based on the source image obtained at 810.

A quantization constrained artificial neural network improved image(constrained artificial image) may be generated at 870. Generating theconstrained artificial image at 870 may include constraining, such aslimiting, truncating, or rounding, values of the artificial imagegenerated at 860 based on the quantization information identified forthe source image at 810.

The constrained artificial image generated at 870 may differ from thequantized source image obtained at 810. A quantized image generated byquantizing the constrained artificial image generated at 870 based onthe quantization information identified for the source image at 810 maybe equivalent to the source image obtained at 810. For example, thedifference between the quantized image generated by quantizing theconstrained artificial image generated at 870 based on the quantizationinformation identified for the source image at 810 and the source imageobtained at 810 may be within a defined similarity threshold, such aszero.

Generating the artificial image generated at 860 and generating theconstrained artificial image generated at 870 may be serially repeated,as indicated by the broken directional line at 875, such as one, two, orthree times, wherein each iteration improves the quality, such as byincreasing image detail, of the generated images relative to a previousiteration. Although shown separately in FIG. 8 , generating theartificial neural network improved image at 860 and generating thequantization constrained artificial neural network improved image at 870may be combined.

The constrained artificial image may be output at 880. For example, theconstrained artificial image generated at 870 may be output forpresentation to a user via display device at 880. Outputting theconstrained artificial image at 880 may include outputting thequantization constrained restoration filtered image such that apresentation of the quantization constrained restoration filtered imageoutput at 850, or the source image output at 820, is replaced by apresentation of the constrained artificial image output at 880.Outputting the constrained artificial image at 880 may include storingor transmitting the constrained artificial image.

The resource utilization for restoration filtering at 830 may be lowrelative to the resource utilization for artificial image generation at860. For example, an iteration of restoration filtering at 830 andrestoration filtering quantization constraint at 840 may utilize 100 or1000 fewer resources than an iteration of artificial image generation at860 and artificial image constraint at 870. In an example, an iterationof restoration filtering at 830 may be performed in ten milliseconds,quantization constraints may be applied in three milliseconds, and theartificial image generation may be performed in one or two seconds.

In some implementations, one or more aspects of quantization constrainedneural image coding 800 may be adjusted based on one or more criteria.For example, the cardinality of iterations of generating the restorationfiltered image at 830 and generating the quantization constrainedrestoration filtered image at 840 may be adjusted. In another example,the cardinality of iterations of generating the artificial neuralnetwork improved image at 860 and generating the quantizationconstrained artificial neural network improved image at 870 may beadjusted, the complexity of generating the artificial neural networkimproved image at 860 may be adjusted, or both.

Adjusting the cardinality of iterations may include using fewer, such aszero or one, iterations based on relatively high source image quality(few blocking or ringing artifacts), based on prioritization of speedover quality, based on resource availability limitations, or acombination thereof. Adjusting the cardinality of iterations may includeusing a greater cardinality of iterations, such as two or moreiterations, based on relatively low source image quality (many blockingor ringing artifacts), based on prioritization of quality over speed,based on high resource availability, or a combination thereof. Thecardinality of iterations of generating the restoration filtered imageat 830 and generating the quantization constrained restoration filteredimage at 840 may be adjusted independently of the cardinality ofiterations of generating the artificial neural network improved image at860 and generating the quantization constrained artificial neuralnetwork improved image at 870.

Adjusting the complexity of generating the artificial neural networkimproved image at 860 may include adjusting the number of layers of theartificial neural network, the number of neurons per layer (or inrespective layers), the block size (or resolution) used, or the like,based on prioritizing for speed, prioritizing for quality, resourceavailability, or a combination thereof. For example, for a relativelylow complexity target, the cardinality of iterations of restorationfiltering at 830, the cardinality of iterations of artificial imagegeneration at 860, or both may be limited, such as to one iteration. Inanother example, for a relatively high complexity target, thecardinality of iterations of restoration filtering at 830, thecardinality of iterations of artificial image generation at 860, or bothmay be increased, such as to greater than three iterations.

As used herein, the terms “optimal”, “optimized”, “optimization”, orother forms thereof, are relative to a respective context and are notindicative of absolute theoretic optimization unless expressly specifiedherein.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.As used herein, the terms “determine” and “identify”, or any variationsthereof, includes selecting, ascertaining, computing, looking up,receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown in FIG. 1 .

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein can occur in various ordersand/or concurrently. Additionally, elements of the methods disclosedherein may occur with other elements not explicitly presented anddescribed herein. Furthermore, one or more elements of the methodsdescribed herein may be omitted from implementations of methods inaccordance with the disclosed subject matter.

The implementations of the transmitting computing and communicationdevice 100A and/or the receiving computing and communication device 100B(and the algorithms, methods, instructions, etc. stored thereon and/orexecuted thereby) can be realized in hardware, software, or anycombination thereof. The hardware can include, for example, computers,intellectual property (IP) cores, application-specific integratedcircuits (ASICs), programmable logic arrays, optical processors,programmable logic controllers, microcode, microcontrollers, servers,microprocessors, digital signal processors or any other suitablecircuit. In the claims, the term “processor” should be understood asencompassing any of the foregoing hardware, either singly or incombination. The terms “signal” and “data” are used interchangeably.Further, portions of the transmitting computing and communication device100A and the receiving computing and communication device 100B do notnecessarily have to be implemented in the same manner.

Further, in one implementation, for example, the transmitting computingand communication device 100A or the receiving computing andcommunication device 100B can be implemented using a computer programthat, when executed, carries out any of the respective methods,algorithms and/or instructions described herein. In addition, oralternatively, for example, a special purpose computer/processor can beutilized which can contain specialized hardware for carrying out any ofthe methods, algorithms, or instructions described herein.

The transmitting computing and communication device 100A and receivingcomputing and communication device 100B can, for example, be implementedon computers in a real-time video system. Alternatively, thetransmitting computing and communication device 100A can be implementedon a server and the receiving computing and communication device 100Bcan be implemented on a device separate from the server, such as ahand-held communications device. In this instance, the transmittingcomputing and communication device 100A can encode content using anencoder 400 into an encoded video signal and transmit the encoded videosignal to the communications device. In turn, the communications devicecan then decode the encoded video signal using a decoder 500.Alternatively, the communications device can decode content storedlocally on the communications device, for example, content that was nottransmitted by the transmitting computing and communication device 100A.Other suitable transmitting computing and communication device 100A andreceiving computing and communication device 100B implementation schemesare available. For example, the receiving computing and communicationdevice 100B can be a generally stationary personal computer rather thana portable communications device and/or a device including an encoder400 may also include a decoder 500.

Further, all or a portion of implementations can take the form of acomputer program product accessible from, for example, a tangiblecomputer-usable or computer-readable medium. A computer-usable orcomputer-readable medium can be any device that can, for example,tangibly contain, store, communicate, or transport the program for useby or in connection with any processor. The medium can be, for example,an electronic, magnetic, optical, electromagnetic, or a semiconductordevice. Other suitable mediums are also available.

The above-described implementations have been described in order toallow easy understanding of the application are not limiting. On thecontrary, the application covers various modifications and equivalentarrangements included within the scope of the appended claims, whichscope is to be accorded the broadest interpretation so as to encompassall such modifications and equivalent structure as is permitted underthe law.

What is claimed is:
 1. An apparatus for image coding using quantizationconstrained neural image coding, the apparatus comprising: a processorconfigured to: generate an artificial image, wherein, to generate theartificial image, the processor is configured to: obtain a source image;identify quantization information from the source image, wherein toidentify the quantization information the processor is configured toidentify multiresolution quantization interval information from thesource image; generate a restoration filtered image, wherein, togenerate the restoration filtered image, the processor is configured torestoration filter the source image; generate a constrained restorationfiltered image, wherein, to generate the constrained restorationfiltered image, the processor is configured to constrain the restorationfiltered image based on the quantization information; obtain anunconstrained artificial image based on the constrained restorationfiltered image and a generative artificial neural network obtained usinga generative adversarial network; and constrain the unconstrainedartificial image based on the quantization information to obtain theartificial image; and output the artificial image.
 2. The apparatus ofclaim 1, wherein: to generate the restoration filtered image theprocessor is configured to: generate a first restoration filtered image,wherein, to generate the first restoration filtered image, the processoris configured to restoration filter the source image; generate a secondrestoration filtered image, wherein, to generate the second restorationfiltered image, the processor is configured to restoration filter afirst constrained restoration filtered image; and generate a thirdrestoration filtered image, wherein, to generate the third restorationfiltered image, the processor is configured to restoration filter asecond constrained restoration filtered image; and to generate theconstrained restoration filtered image the processor is configured to:generate the first constrained restoration filtered image, wherein, togenerate the first constrained restoration filtered image, the processoris configured to constrain the first restoration filtered image based onthe quantization information; generate the second constrainedrestoration filtered image, wherein, to generate the second constrainedrestoration filtered image, the processor is configured to constrain thesecond restoration filtered image based on the quantization information;generate a third constrained restoration filtered image, wherein, togenerate the third constrained restoration filtered image, the processoris configured to constrain the third restoration filtered image based onthe quantization information; and use the third constrained restorationfiltered image as the constrained restoration filtered image.
 3. Theapparatus of claim 1, wherein: to obtain the unconstrained artificialimage the processor is configured to: obtain a first unconstrainedartificial image based on the constrained restoration filtered image;obtain a second unconstrained artificial image based on a firstartificial image; and obtain a third unconstrained artificial imagebased on a second artificial image; and to obtain the artificial imagethe processor is configured to: obtain the first artificial image,wherein, to obtain the first artificial image, the processor isconfigured to constrain the first unconstrained artificial image basedon the quantization information; obtain the second artificial image,wherein, to obtain the second artificial image, the processor isconfigured to constrain the second unconstrained artificial image basedon the quantization information; obtain a third artificial image,wherein, to obtain the third artificial image, the processor isconfigured to constrain the third unconstrained artificial image basedon the quantization information; and use the third artificial image asthe artificial image.
 4. An apparatus for image coding usingquantization constrained neural image coding, the apparatus comprising:a processor configured to: generate an artificial image, wherein togenerate the artificial image, the processor is configured to: obtain asource image; read quantization information from the source image;obtain the artificial image based on the source image, the quantizationinformation, and a machine learning model; and output the artificialimage.
 5. The apparatus of claim 4, wherein, to read the quantizationinformation, the processor is configured to read multiresolutionquantization interval information from the source image.
 6. Theapparatus of claim 4, wherein: to generate the artificial image theprocessor is configured to output the source image prior to outputtingthe artificial image.
 7. The apparatus of claim 4, wherein: the machinelearning model is a generative artificial neural network; and togenerate the artificial image the processor is configured to obtain thegenerative artificial neural network, wherein, to obtain the generativeartificial neural network, the processor is configured to use agenerative adversarial network.
 8. The apparatus of claim 7, wherein togenerate the artificial image the processor is configured to: generatethe artificial image based on a defined cardinality of iterations ofartificial image generation, wherein, to perform a respective iterationof artificial image generation, the processor is configured to: input anartificial image generation input image to the generative artificialneural network; in response to the input of the artificial imagegeneration input image to the generative artificial neural network,obtain an unconstrained artificial image from the generative artificialneural network; and constrain the unconstrained artificial image basedon the quantization information to obtain the artificial image.
 9. Theapparatus of claim 8, wherein, for a first iteration of artificial imagegeneration, the processor is configured to use the source image as theartificial image generation input image.
 10. The apparatus of claim 8,wherein, for each iteration of artificial image generation subsequent tothe first iteration of artificial image generation, the processor isconfigured to use the artificial image obtained by an immediatelypreceding iteration of artificial image generation as the artificialimage generation input image.
 11. The apparatus of claim 8, wherein toconstrain the unconstrained artificial image based on the quantizationinformation the processor is configured to constrain the unconstrainedartificial image based on the quantization information such that adifference between a quantized image generated by quantizing theartificial image based on the quantization information and a quantizedsource image is within a defined similarity threshold.
 12. The apparatusof claim 8, wherein to generate the artificial image the processor isconfigured to: generate a constrained restoration filtered image basedon a defined cardinality of iterations of constrained restorationfiltered image generation, wherein, to perform an iteration ofconstrained restoration filtered image generation, the processor isconfigured to: generate a restoration filtered image, wherein, togenerate the restoration filtered image, the processor is configured torestoration filter a restoration filtering input image; and constrainthe restoration filtered image based on the quantization information toobtain the constrained restoration filtered image.
 13. The apparatus ofclaim 12, wherein: for a first iteration of constrained restorationfiltered image generation the processor is configured to use the sourceimage as the restoration filtering input image; and for a firstiteration of artificial image generation the processor is configured touse the constrained restoration filtered image as the artificial imagegeneration input image.
 14. The apparatus of claim 13, wherein, toperform each iteration of constrained restoration filtered imagegeneration subsequent to the first iteration of constrained restorationfiltered image generation, the processor is configured to use theconstrained restoration filtered image obtained by an immediatelypreceding iteration of constrained restoration filtered image generationas the restoration filtering input image.
 15. The apparatus of claim 12,wherein to constrain the restoration filtered image based on thequantization information the processor is configured to constrain therestoration filtered image based on the quantization information suchthat a difference between a quantized image generated by quantizing theconstrained restoration filtered image based on the quantizationinformation and a quantized source image is within the definedsimilarity threshold.
 16. The apparatus of claim 12, wherein to generatethe artificial image the processor is configured to output theconstrained restoration filtered image prior to outputting theartificial image.
 17. An apparatus for image coding using quantizationconstrained neural image coding, the apparatus comprising: a processorconfigured to: generate an artificial image, wherein to generate theartificial image the processor is configured to: obtain a source image;identify quantization information from the source image; generate aconstrained restoration filtered image based on a defined cardinality ofiterations of constrained restoration filtered image generation,wherein, to perform each iteration of constrained restoration filteredimage generation the processor is configured to: generating arestoration filtered image by restoration filtering a restorationfiltering input image, wherein the processor is configured to: on acondition that the iteration of constrained restoration filtered imagegeneration is a first iteration of constrained restoration filteredimage generation, use the source image as the restoration filteringinput image; and on a condition that the iteration of constrainedrestoration filtered image generation is an iteration of constrainedrestoration filtered image generation subsequent to the first iterationof constrained restoration filtered image generation, use theconstrained restoration filtered image obtained by an immediatelypreceding iteration of constrained restoration filtered image generationas the restoration filtering input image; and constrain the restorationfiltered image based on the quantization information to obtain theconstrained restoration filtered image; obtain the artificial imagebased on an artificial image generation input image, the quantizationinformation, and a generative artificial neural network, wherein toobtain the artificial image the processor is configured to perform adefined cardinality of iterations of artificial image generation,wherein, to perform each iteration of the artificial image generation,the processor is configured to: input the artificial image generationinput image to the generative artificial neural network, wherein theprocessor is configured to: on a condition that the iteration of theartificial image generation is a first iteration of artificial imagegeneration, use the constrained restoration filtered image as theartificial image generation input image; and on a condition that theiteration of the artificial image generation is an iteration ofartificial image generation subsequent to the first iteration ofartificial image generation, use the artificial image obtained by animmediately preceding iteration of artificial image generation as theartificial image generation input image; in response to the input of theartificial image generation input image to the generative artificialneural network, obtain an unconstrained artificial image from thegenerative artificial neural network; and constrain the unconstrainedartificial image based on the quantization information to obtain theartificial image; and output the artificial image.
 18. The apparatus ofclaim 17, wherein to generate the artificial image the processor isconfigured to obtain the generative artificial neural network using agenerative adversarial network.
 19. The apparatus of claim 17, wherein:to constrain the unconstrained artificial image based on thequantization information the processor is configured to constrain theunconstrained artificial image based on the quantization informationsuch that a difference between a quantized image generated by quantizingthe artificial image based on the quantization information and aquantized source image is within a defined similarity threshold; and toconstrain the restoration filtered image based on the quantizationinformation the processor is configured to constrain the restorationfiltered image based on the quantization information such that adifference between a quantized image generated by quantizing theconstrained restoration filtered image based on the quantizationinformation and a quantized source image is within the definedsimilarity threshold.
 20. The apparatus of claim 17, wherein to generatethe artificial image the processor is configured to: output the sourceimage prior to outputting the constrained restoration filtered image;and output the constrained restoration filtered image prior tooutputting the artificial image.